Why the Explosion In AI? Dr. Michael Housman Speaks to General Assembly

In a talk with General Assembly, Dr. Michael Housman, Chief Data Scientist at RapportBoost.AI, explains the exponential growth curves that sit behind the recent explosion in artificial intelligence. He argues that AI’s rapid emergence is not the result of a single breakthrough, but the compounding effect of several accelerating trends.

At the core of this acceleration is the exponential increase in computing power. Advances in GPUs and specialized hardware have made it possible to train far more complex models at a fraction of the cost and time previously required. What once took weeks or months can now be accomplished in hours or days.

A second exponential curve comes from data availability. As more of human activity moves online—through chat, messaging, voice, transactions, and sensors— the volume of labeled and semi-labeled data grows dramatically. These large datasets are the fuel that modern machine learning and deep learning systems depend on.

Housman also highlights the role of algorithmic innovation. Improvements in neural network architectures, optimization techniques, and transfer learning mean that models are not only getting more powerful, but also more efficient. Each generation of algorithms builds on the last, creating a compounding effect rather than linear progress.

When these curves—compute, data, and algorithms—intersect, the result is a step-change in capability. Systems that once struggled with language, perception, or pattern recognition suddenly cross a threshold where performance feels human-like or even superhuman in narrow domains.

Housman cautions, however, that exponential growth can be misleading. Early stages often appear slow and unimpressive, leading organizations to underestimate the impact. Once the curve bends upward, change feels sudden and disruptive. Companies that fail to recognize this pattern risk being caught unprepared.

He concludes by encouraging leaders to rethink planning assumptions. In an exponential world, incremental thinking breaks down. Organizations must experiment early, invest in learning, and build adaptability into their strategies if they want to ride the AI wave rather than be overwhelmed by it.